"""Contains a variant of the densenet model definition."""
#logits：https://www.tinymind.com/executions/k6omvsyx

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import numpy as np

slim = tf.contrib.slim


def trunc_normal(stddev): return tf.truncated_normal_initializer(stddev=stddev)


def bn_act_conv_drp(current, num_outputs, kernel_size, scope='block'):
    current = slim.batch_norm(current, scope=scope + '_bn')
    current = tf.nn.relu(current)
    current = slim.conv2d(current, num_outputs, kernel_size, scope=scope + '_conv')
    current = slim.dropout(current, scope=scope + '_dropout')
    return current


def block(net, layers, growth, scope='block'):
    for idx in range(layers):
        bottleneck = bn_act_conv_drp(net, 4 * growth, [1, 1],
                                     scope=scope + '_conv1x1' + str(idx))
        tmp = bn_act_conv_drp(bottleneck, growth, [3, 3],
                              scope=scope + '_conv3x3' + str(idx))
        net = tf.concat(axis=3, values=[net, tmp])
        print(net.get_shape())
    return net


def transfer_layer(net, channel ,scope=None):
    
    with tf.name_scope("transfer_layer" + scope) as scope:
        with slim.arg_scope(densenet_arg_scope()):
    
            net = slim.conv2d(net, channel, [1, 1], scope=scope,
                              activation_fn=tf.nn.relu)
            #print(input_.name)
    
    with tf.name_scope("average_pool" + scope) as scope:
        net = slim.avg_pool2d(net, [2, 2], stride=2, padding="VALID", scope=scope)
        #print(input_.get_shape())
    
    return net


def densenet(images, num_classes=1001, is_training=False,
             dropout_keep_prob=0.8,
             scope='densenet'):
    """Creates a variant of the densenet model.

      images: A batch of `Tensors` of size [batch_size, height, width, channels].
      num_classes: the number of classes in the dataset.
      is_training: specifies whether or not we're currently training the model.
        This variable will determine the behaviour of the dropout layer.
      dropout_keep_prob: the percentage of activation values that are retained.
      prediction_fn: a function to get predictions out of logits.
      scope: Optional variable_scope.

    Returns:
      logits: the pre-softmax activations, a tensor of size
        [batch_size, `num_classes`]
      end_points: a dictionary from components of the network to the corresponding
        activation.
    """
    growth = 32
    compression_rate = 0.5

    def reduce_dim(input_feature):
        return int(int(input_feature.shape[-1]) * compression_rate)

    end_points = {}

    with tf.variable_scope(scope, 'DenseNet', [images, num_classes]):
        
        net = slim.conv2d(images, 34, [7, 7], 2, scope="first_conv_layer",
                          activation_fn=tf.nn.relu,
                          weights_initializer=tf.contrib.layers.variance_scaling_initializer(factor=2.0, mode='FAN_IN', uniform=False))
        end_points["first_layer"] = net
            
        net = slim.max_pool2d(net, [2, 2], 2, scope="first_max_pooling")
        end_points["first_max_pool"] = net
        
        with slim.arg_scope(bn_drp_scope(is_training=is_training,
                                         keep_prob=dropout_keep_prob)) as ssc:  
            
            with slim.arg_scope(densenet_arg_scope(weight_decay=0.004)):
            
                net = block(net, 6, growth=growth, scope='block1')
                end_points["block1"] = net
            
                net = transfer_layer(net, channel=reduce_dim(net) ,scope="tf_layer_1")
                end_points["tf_layer_1"] = net
            
                net =  block(net, 12, growth=growth, scope='block2')
                end_points["block2"] = net
            
                net = transfer_layer(net, channel=reduce_dim(net) ,scope="tf_layer_2")
                end_points["tf_layer_2"] = net
            
                net =  block(net, 24, growth=growth, scope='block3')
                end_points["block3"] = net
            
                net = transfer_layer(net, channel=reduce_dim(net) ,scope="tf_layer_3")
                end_points["tf_layer_2"] = net
            
                net =  block(net, 16, growth=growth, scope='block4')
                end_points["block4"] = net
                        
        with tf.name_scope("classifer_layer_avg") as scope:
            net = slim.avg_pool2d(net,  [7, 7], 1, padding="VALID", scope=scope)
            net = tf.squeeze(net, [1, 2])
     
        with tf.name_scope("classifer_layer_softmax") as scope:
            net = slim.fully_connected(net, num_classes, scope=scope)
    
        with tf.name_scope("softmax") as scope:
            logits = slim.softmax(net, scope=scope)
            
    return logits, end_points


def bn_drp_scope(is_training=True, keep_prob=0.8):
    keep_prob = keep_prob if is_training else 1
    with slim.arg_scope(
        [slim.batch_norm],
            scale=True, is_training=is_training, updates_collections=None):
        with slim.arg_scope(
            [slim.dropout],
                is_training=is_training, keep_prob=keep_prob) as bsc:
            return bsc


def densenet_arg_scope(weight_decay=0.004):
    """Defines the default densenet argument scope.

    Args:
      weight_decay: The weight decay to use for regularizing the model.

    Returns:
      An `arg_scope` to use for the inception v3 model.
    """
    with slim.arg_scope(
        [slim.conv2d],
        weights_initializer=tf.contrib.layers.variance_scaling_initializer(
            factor=2.0, mode='FAN_IN', uniform=False),
        activation_fn=None, biases_initializer=None, padding='same',) as sc:
        return sc

densenet.default_image_size = 224